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Record W2167009844 · doi:10.1021/ja904185b

Hydrogen-Bonding Asymmetric Metal Catalysis with α-Amino Acids: A Simple and Tunable Approach to High Enantioinduction

2009· article· en· W2167009844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Chemical Society · 2009
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Synthesis and Catalysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryCatalysisCombinatorial chemistryLigand (biochemistry)SelectivityHydrogen bondBrønsted–Lowry acid–base theoryMetalAmino acidEnantioselective synthesisChiral ligandCopperAlkynylationTransition metalOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

While asymmetric transition-metal catalysis has become a powerful method for constructing chiral products, a challenge in this field is the identification of the correct ligand for high selectivity. We report here a simple approach to chiral catalyst formation: coupling of an available pool of Brønsted acids, namely, amino acid derivatives, with tunable ligands on copper catalysts. This system can be used to generate many different chiral environments simply by changing the amino acid or ligand employed and provides a scaffold for rapid screening and identification of the correct combination for high enantioselectivity. The latter is illustrated in the copper-catalyzed alkynylation of imines in up to 99% ee.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.217
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it